Learning to Classify Biomedical Terms Through Literature Mining and Genetic Algorithms.

We present an approach to classification of biomedical terms based on the information acquired automatically from the corpus of relevant literature. The learning phase consists of two stages: acquisition of terminologically relevant contextual patterns (CPs) and selection of classes that apply to terms used with these patterns. CPs represent a generalisation of similar term contexts in the form of regular expressions containing lexical, syntactic and terminological information. The most probable classes for the training terms co-occurring with the statistically relevant CP are learned by a genetic algorithm. Term classification is based on the learnt results. First, each term is associated with the most frequently co-occurring CP. Classes attached to such CP are initially suggested as the term’s potential classes. Then, the term is finally mapped to the most similar suggested class.

• Type: Conference
• Year: 2004
• Where: IDEAL
• Authors: Irena Spasic, Goran Nenadic, Sophia Ananiadou
• Keywords: IDEAL 2004, Relevant Contextual Patterns, Similar Term Contexts, Term’s Potential Classes